Production and Productivity Growth in Chinese Agriculture:

Production and Productivity Growth in Chinese Agriculture:

New National and Regional Measures

Shenggen Fan*

Xiaobo Zhang*

International Food Policy Research Institute

2033 K Street, NW

Washington, D.C. 20006 U.S.A.

(Forthcoming in EDCC and revised on June 19, 2001)

Production and Productivity Growth in Chinese Agriculture:

New National and Regional Measures

I. Introduction

Agricultural output in China has been reported growing rapidly in the past several decades, particularly since the rural reforms that began in 1979. The State Statistical Bureau (SSB 1998) reported that from 1952 to 1997, output grew at 4.4% per annum. Up until 1978, the annual rate was 2.8% per annum. It then jumped to 6.5% per annum during the period from 1979 to 1997. This long-term growth rate was one of the highest worldwide during the same period.

There have been numerous literatures in explaining China’s dramatic agricultural growth. Most studies (Weins 1982, Lardy 1983, Perkins 1984, Tang 1984, Lin 1988 and 1992, Fan 1990 and 1991, World Bank 1991, Wen 1993, and Zhang and Carter 1997) on Chinese agriculture have used gross value of agricultural output (GVAO) as an indicator to measure growth in agricultural output. GVAO is reported by the State Statistical Bureau (SSB) --- the official government agency specialized in collecting, measuring, and reporting statistics in China. However, GVAO is usually measured in constant prices (or comparable prices as described by the Chinese statistical system) to represent total output in a particular year. But constant prices may not be the appropriate weights in aggregating total output because the growth rates calculated from these constant prices may be seriously biased, especially when relative prices have changed (Diewert 1976, Lau 1979, and Jorgenson 1995). Relative output prices in Chinese agriculture have changed dramatically over time and across regions since the economic reform in the late 1970s.

In more recent years, many scholars have also questioned the accuracies of livestock and fishery output in Chinese official statistics. For example, Zhong (1997), Lu (1998), and Fuller et al. (2000) estimated that major livestock output reported by SSB may have been overstated by as large as over 40%, and fishery output by 70% in 1996.

Biased estimates of production and productivity growth in Chinese agriculture may have serious consequences, as most previous studies have used these indicators to judge the performance of the agricultural sector and to measure the impact of the rural reforms in Chinese agriculture. Moreover, since the officially published output values measured in constant prices are also widely used in the regional inequality literature, a regional inequality index calculated based on official data may be misleading as well.

This paper aims to properly measure national and regional growth in output, input, and total factor productivity in Chinese agriculture, and to reassess the impact of the recent policy reforms on production and productivity growth and regional inequality. The paper is organized as follows. The next section will review some aggregation issues in production theory. We show how estimates of aggregate output and input, and therefore measured total factor productivity, can be biased as a result of using constant prices as weights. The third section will be devoted to the measurement of growth in output, input and productivity, and regional inequality in Chinese agriculture. We conclude the paper in section 4.

II. Conceptual Framework in Output and Input Aggregation

The agricultural sector in China produces a great number of products including major staple grains like rice, wheat, and corn, major livestock products such as meat, eggs, and diary products, and horticultural and fishery products. Aggregation is often needed in order to compare the performance of the whole agricultural sector over time and across regions. Similarly, aggregation over inputs used in the agricultural sector is also necessary for number of reasons, such as providing information for measuring technical changes and efficiency improvement, and for measuring total factor productivity.

Agricultural output in the Chinese statistical reporting system is measured as gross value of agricultural output (GVAO) by summing production values of all products produced in the sector. Production value of a certain product is calculated by multiplying quantity by price. GVAO is often measured in current prices, and for the purpose of comparison across years, it is also measured and reported in constant prices.

Many economists have pointed out that using constant prices to aggregate output may result in biased estimate of production growth. Despite these concerns, many countries and international organizations still report growth in output aggregated using constant prices. This potential bias is illustrated in Figure 1 where Q0 represents a production possibility curve, which indicates the different combination of products Y1 and Y2, using the same amount of inputs. Profit-maximizing producers choose different combination of Y1 and Y2 based on relative prices of the two products. Producers would choose point a in the production possibility curve when relative prices are P1, and b when relative prices are P2. If total output is aggregated using a liner aggregation of the two products weighted by their relative prices P1, aggregate output at a (equals to output at b') would be greater than that at b. But if P2 were used in the aggregation, output at b (equals to output at a') would be greater than that at a. Different output measures are obtained using different price weights, although producers only move along the same production possibility curve.

Figure 2 shows the potential bias that arises from input aggregation where I0 represents an isoquant in which the same amount of output is produced using different input combination, X1 and X2. Cost-minimizing producers choose input combination based on relative input prices, W1 and W2. If producers face relative prices W1, then the optimal combination of inputs would be at point c. If relative prices change to W2, the optimal combination of inputs would be at d. This shift is the producers' response to input price changes (the substitution effect) along the same isoquant. But using different relative prices as weights yields different input aggregates. For example if relative prices W1 are used as weights, aggregated input at d is greater than that at c (equals to output at d'). Conversely, if the relative price W2 is used, aggregate input at c is greater than that at d (equals to output at c'). The resulting productivity index using these biased estimates of aggregate output and input is also biased even when there has been no change in quantities of either inputs or outputs.

In order to minimize the potential bias caused by relative price changes, several approaches have been developed in the literature. The most commonly used method is the Divisa index. As Richter (1966) has shown, the Divisia index is desirable because of its invariance property: if nothing real has changed (e.g., the only quantity changes involve movements around an unchanged isoquant) then the index itself is unchanged. In practice, the Törnqvist-Theil (TT) index is usually used to approximate the Divisia index. The formula for a TT index of aggregate output is:

),

Y

/

Y

(

)*

S

+

S

(

*

1/2

=

lnQI

1

-

t

i,

t

i,

1

-

t

i,

t

i,

i

t

ln

S

(1)

where lnQIt is the log of the aggregate output index at time t, S i, t and Si, t-1 are output i's share in total production value at time t and t-1, respectively. Yi ,t and Yi, t-1 are quantities of output i at time t and t-1, respectively. The advantage of such an index is that rolling weights accommodate any substantial changes in relative prices over time. Diewert (1976) and Lau (1979) have proved that the TT index is exact for the more general class of translog aggregator functions. The TT index of aggregated input growth can be expressed in a similar way.

Based on the growth of aggregated output and input, total factor productivity (TFP) is defined as the difference of these two. Specifically, the TFP index can be written as follows.

)

X

/

X

(

*

)

W

+

W

(

*

1/2

-

)

Y

/

Y

(

*

)

S

+

S

(

*

1/2

=

lnTFP

1

-

t

i,

t

i,

1

-

t

i,

t

i,

i

1

-

t

i,

t

i,

1

-

t

i,

t

i,

i

t

ln

ln

S

S

(2)

Where lnTFPt is the log of total factor productivity index, Wi,t and W i,t-1 are the cost shares of input i in total cost at time t and t-1, respectively, and Xi,t and X i,t-1 are the quantities of input i at time t and t-1, respectively.

III. Measures of Growth in Output, Input and Productivity, and Changes in Regional Inequality

3.1.Output Measures and Adjustments

As defined by SSB, GVAO refers to total volume of output of farming, forestry, animal husbandry, and fishery in value terms, reflecting the scale of and the achievements made in agricultural production during a given period of time. The scope of official statistics on farming, forestry, animal husbandry, and fishery is as follows:

Farming is defined as cultivation of farm crops and other agricultural activities. Farm crops include grains, cotton, oil-bearing crops, sugar crops, bast fiber plants, tobacco, vegetables, medicinal herbs, melon and gourd crops, and cultivation and management of tea plantations, mulberry fields, and orchards. Other agricultural activities are defined as harvesting wild vegetation fruits, fiber, gum, resin, oil-bearing plants, grass, wild medicinal herbs, fungus plants, and rural household nonfarm activities.

Forestry refers to planting trees of various kinds (excluding tea plantations, mulberry fields and orchards), collection of forestry products, and cutting and felling of bamboo and trees by villages and other cooperative organizations under villages. Animal husbandry refers to raising and grazing of all animals except fishing and cultivating, and hunting and raising of wild animals. Fishery refers to cultivation and catching of fish and other aquatic animals and cultivation and collection of seaweed and other aquatic plants.

As indicated in the last two sessions, GVAO measured in constant prices cannot reflect the real growth of agricultural production. In this study, we use the TT index to measure production growth in Chinese agriculture for the past several decades. There are several hundred agricultural products covered by SSB, we try to cover as many as products as we can. In addition, adjustments have to be made to avoid double counting of livestock and fishery output. The following is a list of the commodities we covered and adjustments made to correct overstated output in this study.

Crops are divided into grain and cash crops. Grain crops include rice, wheat, corn, sorghum, millet, soybean, and tubers. Cash crops include cotton, hemp, jute, rapeseed oil, peanuts, sugarcanes, sugar beets, apples, bananas, citrus, grapes, pears, tea, tobacco, and vegetables. Livestock includes pork, beef, mutton, poultry, eggs, cow milk, and goat milk. Zhong (1997) found that meat output might have been overstated by 40% in 1996’s official statistics by comparing consumption data from rural and urban households surveys and official reported meat production. Lu (1998) estimated that official meat and egg output was overestimated by 52% in 1995. Fuller et al. (2000) further estimated meat output by different types and found that 1996 official statistics overstated output of pork by 65%, beef by 139%, poultry by 239%, mutton by 87%, and eggs by 64% and most of livestock products were underreported back in 1985. First Agricultural Census data also show that the inventories of large animals have been overstated by 30%, swine by 36%, goats and sheep by 40%. However, the inventory data does not reflect overstated number of animals slaughtered, and weight per head when slaughtered. Therefore, in this study, we use Fuller et al.’s estimates to adjust the official statistics for pork, beef, mutton, poultry, and eggs. The SSB has adjusted its meat production data since 1996 beginning in 1998’s China Statistical Yearbook. For 1997 livestock output data, we simply use the growth rate reported by SSB to extrapolate the output data, assuming there is no over reporting of growth in 1997. Fishery products are total weights of both sea and fresh water fish and other aquatic animals produced in a year measured in tons. We use Lu’s estimate of fishery output to adjust official fishery output. Forestry products are excluded in this study. The exclusion of forestry does not bias estimate too much, because it accounts for only 3% of total SSB's GVAO in 1997.

The official quantity data are taken from various issues of China’s Statistical Yearbook and China’s Agricultural Yearbook. Prices are taken from China Commodity Price Yearbooks (various issues), China Trade and Price Statistical Materials, 1952-83, China Domestic Marketing Statistical Yearbooks (various issues), and USDA Agricultural Statistics of the People's Republic of China, 1949-90.

Table 1 reports various measures of output growth for China as a whole. The SSB index is reported by various official statistics without adjusting meat and fishery output. The constant price index was calculated by the authors using the official price and output data (no adjustments were made on meat and fishery data) and 1980 constant prices in aggregation. The TT1 is the output index constructed using the Törnqvist-Theil index approach and official statistics without adjusting meat and fishery output. The TT2 index is the Törnqvist-Theil index constructed by the authors using adjusted livestock and fishery output data. The difference between SSB index and index measured in 1980 prices is solely due to the commodity coverage because the methodology used in aggregating these two indexes are the same. When comparing SSB’s GAVO and ours, we found the difference ranges from 20% in 1952, 1% in 1980, to 7% in 1997. Except for the 1950s, our coverage is reasonably good. The reason for large difference in the 1950s is due mainly to the fact that SSB’s GAVO includes values of manurial fertilizer and rural household’s small industry, and they are not in our commodity coverage and have been excluded from SSB’s GAVO since 1957.

The difference between the index measured in 1980 prices and TT1 index is due to the aggregation bias as we have illustrated in the previous section. Prior to the 1960s, the difference between these two indexes is small, being less than 2%. But the disparity grew larger over time. Particularly since the reforms, the difference has grown from less than 8% in 1978 to more than 31% in 1997, implying that part of the rapid growth in agricultural output measured in constant prices may come from the aggregation bias. There have been rapid price changes in agricultural products in Chinese agriculture since the reforms. Some of the substitution effects (or price effects) may have been captured as part of the production increase if constant prices are used in the aggregation.

The difference between TT1 and TT2 is due to the adjustments made to livestock and fishery output. The TT1 uses the official statistical meat and fishery data, while TT2 uses the meat and fishery data adjusted by the authors to avoid the over reporting of these outputs. The difference in 1981 is less than 1%, but gradually increased to 18% in 1997. Prior to 1988, agricultural production value was underreported while it was overstated thereafter due to the adjustment of livestock and fishery data.

Although still growing at a respective rate, the appropriately measured production growth is 4.6% per annum from 1979 to 1997, compared to 6.5% per annum reported by official SSB publications. In other words, the annual agricultural production growth reported by official statistics has been overstated as high as 1.9 % per annum from 1979 to 1997.

Table 2 reports measures of output growth at the provincial level. Due to data unavailability, we were able to construct provincial growth index only after 1979. But this may not be a serious problem because overestimation of production growth predominantly occurred after the reforms. The constant price index represents measures of growth using the 1980 constant prices and official SSB output data without adjusting meat and fishery output. The TT2 index is measured by the authors using the Törnqvist-Theil index approach and the adjusted SSB output data. One significant finding from the table is that overestimation has mainly occurred in less-developed areas. For example, growth in GAVO in Guizhou and Qinghai has been overestimated by more than 4% per annum, and that in Gansu has been overestimated by almost 3% per annum. This implies that the difference in development level between less- and more-developed areas is actually larger than that calculated from the official statistics.

3.2Measures of Inputs

In order to aggregate total input, both quantities and prices of individual inputs are required. Quantities are easy to obtain as SSB has been consistently publishing these input data since the 1950s. But prices of inputs are not readily available. Certain statistical techniques are required to extrapolate price data for some inputs.

3.2.1Quantities of Inputs

Labor Input. Labor input is measured in person-year equivalent of workers directly engaged in production of farming, animal husbandry, and fishery. The labor of rural industry is excluded from agricultural labor input.

Land Input. Land input is measured as arable land. The official data on arable land are extremely inaccurate, and is reported to be understated by 30-40% from various sources. However, as long as the over reporting of land quantity is constant over time and the land input cost share is measured as residual as we will discuss later, the measure of input growth index will not be affected.

Machinery Power Input. It refers to total mechanical power of machinery used in farming, animal husbandry, sideline occupations and fishery, including ploughing, irrigation and drainage, harvesting, farm product processing and transportation, plant protection, stockbreeding, and fishery. It excludes machinery used for non-agricultural purposes such as township- and village-run industry, construction, non-agricultural transportation, scientific experimentation and teaching.

Capital of Animals. It is the sum of capitals for all animals. For draft animals and milk cows, 20% of current animal value is used as capital (assuming an animal can be used for 5 years). For hogs, beef cattle, sheep and goats, and poultry, the year-ending values of animal stocks for the previous year are used as capital input. For breeding animal stocks, 33.3% of the current values of animals are added to the capital assuming the animals can be used for 3 years.

Chemical Fertilizer Input. Chemical fertilizer is measured in standard weights. The standard weights are obtained by converting actual weights into the following standard: ammonium sulfate (20% nitrogen), super-phosphate (18.7% P2O2), and potassium sulfate (40% K2O).

Pesticide Input. It is measured in standard weight, and includes both pesticides and fungicides.

Seed Input. Seed input is calculated based on the average seed use per hectare of sown area reported by the Handbooks of Agricultural Technical Economics.

Feed Input. Total feed input is calculated based on number of animals and per day use of feed for each type of animals reported by the Handbook of Agricultural Technical Economics.

Irrigation Input. Irrigation input is the total irrigated area in agriculture, i.e., those areas that are effectively irrigated – level land which has water source and complete sets of irrigation facilities to lift and move adequate water for irrigation purposes under normal conditions.

3.2.2Prices of Inputs

Labor Price. Labor price is measured as wage per day. Wages for 1953, 1957, 1962, 1965, and from 1975 to 1997 annually are reported by State Price Bureau in its Production Cost Survey. The gaps are estimated using geometric extrapolation. The annual labor cost is calculated by multiplying daily wage by number of working days per person.

Chemical Fertilizer Price. Chemical fertilizer price is measured as yuan per standard ton. The data after 1977 are reported by China Agricultural Development Report (1995 -98), and those prior to 1978 are reported in the China Trade, Marketing, and Price Statistical Materials, 1952-1983.

Pesticide Price. Pesticide price is measured as yuan per standard ton. The data after 1977 are reported by China Agricultural Development Report (1995 -98), and those prior to 1978 are reported in the China Trade, Marketing, and Price Statistical Materials, 1952-1983.

Machinery Input Price. Data on agricultural machinery price measured as yuan per horsepower after 1985 are reported in China Marketing Statistical Yearbooks. Those prior to 1985 are estimated by the author using price index of agricultural production materials reported in various statistical yearbooks published by SSB. When calculating the total cost of machinery use, 5 year-life span of machinery equipment is assumed, i.e., 20% of total machinery value is used as flow (or service) terms cost.

Seed Price. Procurement prices for grains, and cash crops are used as prices for seed.

Feed Price. Procurement prices for grains are used as prices for feed.

Land Price. Since there has been no land market in China, we use residual of total production value net of costs for labor, fertilizer, machinery, and irrigation on a per unit of land base as land price.

Animal Stock Price. Average prices for different types of animals are used as prices for animal capitals.

Irrigation price. This is measured as irrigation fee per irrigated hectare. Irrigation fee per hectare is reported by the Production Cost Survey (State Price Bureau, 1953-98), while irrigated areas are reported by various China Statistical Yearbooks.

3.2.3 Aggregation of Inputs

Total input in agriculture is usually aggregated using the shares of inputs as weights. Changes in input shares show that Chinese agriculture has experienced rapid technical transformation in the past several decades (Figure 3). Farmers have increased their use of modern inputs such as chemical fertilizer, pesticides, and machinery. The share of chemical fertilizer has increased from almost none in 1952 to more than 11% of the total production cost in 1997. The share of machinery has also increased from almost zero in 1957 to more than 5% in 1977. The declining share of machinery (3% in 1997) in recent years is mainly a result of more efficient allocation of inputs by farmers when production was decentralized to households. This is consistent with Fan and Ruttan's findings that a centrally planned economy often overuses capital because of the ideology belief. The rapid decline of pesticide use since 1982 is mainly due to the improvement of pesticide quality and more efficient application by farmers due to the change of production objectives from maximizing yield during the pre-reform period to maximizing profit during the reform period.

Meanwhile, the importance of traditional inputs like labor and land has declined rapidly. The land cost share declined from more than 53% in the 1950s to only 35% in 1997. The labor cost share first increased from 32% in 1952 to 45% in 1983, then declined again to 30% in 1997.

Total input is aggregated using both the TT approach and 1980 constant shares (table 3). The 1980 constant share index overestimates growth in total input use by more than 1.18% per annum from 1952 to 1997. This bias was especially large during the pre-reform period (by 2.07% per annum). This is because the constant share index captures part of the substitution effects among inputs as input growth. Table 4 shows that the underestimation of inputs has less regional difference than the overestimation of output.

We also constructed the aggregate input index using Wen’s weights. The difference between his and our measures comes from both individual input use and shares used in the aggregation. But most of the difference comes from the weights. In fact, Wen’s index is not that much different from the TT index up to 1964, implying that Wen’s weights might be closer to the actual input shares in the 1950s and early 1960s.

3.3Measures of Total Factor Productivity

Using estimated aggregate output and input growth; total factor productivity growth can be easily calculated. Again, both 1980 constant prices (or shares) and Törnqvist-Theil indexes are calculated at both national and provincial levels (tables 5 and 6). The TT1 index is the TFP index using the TT1 output growth index from Table 1 and the TT input growth index from Table 3, while TT2 is the TFP index using TT2 output growth from Table 1 and TT input growth index from Table 3. The 1980 constant price (share) index would underestimate total factor productivity growth by only 0.55% per year from 1952 to 1997, but this bias has great variations over time. The constant price (share) index would overestimate the decline in total factor productivity by more than 1.67% per annum from 1952 to 1978 and overstate the growth by more than 1.08% per annum from 1979 to 1997 when compared with the TT1 index. This overestimated total factor productivity growth after 1979 would exaggerate the effects of the rural reforms on productivity growth substantially. The difference between TT1 and TT2 is mainly due to the difference in livestock output between official statistics and adjusted output. Over reported livestock output would overestimate TFP growth by 0.98 percent per annum from 1979 to 1997.

The regional difference in TFP growth is larger than that in both output and input growth due to overestimation of output and underestimation of inputs. In Guizhou, and Qinghai, TFP growth has been overestimated by over 4% per annum, when constant prices (shares) instead of TT indexes are used. This indicates that the official statistics have underestimated the difference in productivity growth between developed and less-developed region in Chinese agriculture.

3.4 Measures of Regional Inequality

Multiplying the TT2 growth index with output values in 1980 prices, we can obtain adjusted output values expressed in 1980 prices. The adjusted values and official GVAOs in the 1980 prices can then be used for inequality analysis. Since each province varies by size, it does not make sense to calculate regional inequality using total outputs. Therefore, we use labor productivity to analyze the regional difference. The results show that official output data tends to understate the regional development gap. For instance, the gaps in labor productivity between the highest Jiangsu and the lowest Gansu provinces calculated from the adjusted data and official data are 5.9 and 4.5 times, respectively, in 1997. Similar bias applies to the estimate of regional inequality index as well. Figure 4 graphs the Gini coefficients based on the two series. At least two features are apparent. First, the inequality index based on adjusted data is always higher than that based on official output data. Second, the rate of increase in regional inequality using the adjusted production value is much faster than that calculated from the official data. Using official output values, the estimated Gini coefficient increases by only 4% from 1980 to 1997, but it increases by 25% if the adjusted data are used. The results indicate that the extent of regional inequality is much more serious than previously thought.

IV. Conclusions

This study uses a more appropriate approach and adjusted livestock and fishery output data to measure growth in output, input and total factor productivity for Chinese agriculture based on detailed quantity and price information. Our results show that the official statistics overestimates both aggregate output and input, resulting in biased estimates of total factor productivity growth. Furthermore, the official data would overstate the impact of the rural reforms on both production and productivity growth. Nevertheless, both production and productivity still grew at respectable rates during the reform period.

The regional measures indicated that it is the less-developed areas where total output growth is overestimated and total input use is underestimated. As a result, growth in total factor productivity has been overestimated. This has several implications. First, the regional inequality in rural China may be larger than that calculated form official statistics. Second, the less-developed areas may have benefited less from the reforms than previously thought.

The overestimation of production and productivity growth may have serious policy consequences on searching the future sources of growth. The overestimation may exaggerate the impact of rural reforms and therefore underestimates the impact of technical change on growth, leading to underinvestment in agricultural research. The underestimated regional inequality may also lead to misinformed decision on more equitable development among regions.

Table 1: Measures of Output Growth at the National Level, 1952=100

Year

SSB

Constant Price

TT1

TT2

1952

100

100

100

100

1953

103

100

100

100

1954

106

104

103

103

1955

114

114

112

112

1956

119

119

118

118

1957

129

120

119

119

1958

127

125

125

125

1959

110

111

111

111

1960

96

91

92

92

1961

94

81

82

82

1962

100

89

89

89

1963

112

98

97

97

1964

127

111

109

109

1965

137

121

118

118

1966

149

133

129

129

1967

151

137

133

133

1968

147

141

136

136

1969

149

147

141

141

1970

172

149

143

143

1971

188

154

147

147

1972

185

154

147

147

1973

200

166

157

157

1974

207

175

165

165

1975

192

182

170

170

1976

190

180

167

167

1977

189

181

168

168

1978

206

200

185

185

1979

222

216

197

197

1980

225

223

202

202

1981

240

240

216

218

1982

267

263

235

238

1983

287

280

249

252

1984

322

310

272

275

1985

333

326

279

286

1986

345

331

283

293

1987

365

352

297

306

1988

379

365

299

300

1989

391

357

315

313

1990

421

421

343

339

1991

436

440

359

352

1992

463

469

375

363

1993

500

514

400

375

1994

543

578

425

385

1995

602

638

465

408

1996

659

669

502

436

1997

693

685

522

441

Annual Growth Rate

1952-78

2.82

2.71

2.38

2.38

1979-97

6.53

6.63

5.56

4.57

1952-97

4.39

4.37

3.74

3.35

Notes: The SSB index is reported by various official statistics without adjusting meat and fishery output. The constant price index was calculated by the authors using the official price and output data (no adjustments were made on meat and fishery data) and 1980 constant prices in aggregation. The TT1 is the output index constructed using the Törnqvist-Theil index approach with official statistics without adjusting meat and fishery output. The TT2 index is the Törnqvist-Theil index constructed by the authors with adjusted livestock and fishery output data.

Table 2: Measures of Output Growth at the Regional Level, 1979-1997

Province

Constant Price

TT2

Overestimation

Annual Growth Rates

Hebei

7.29

5.23

2.06

Shanxi

5.46

3.32

2.13

Inner Mongolia

10.09

7.27

2.82

Liaoning

6.23

4.49

1.74

Jilin

4.99

3.54

1.45

Heilongjiang

6.11

4.60

1.50

Jiangsu

5.91

4.41

1.50

Zhejiang

6.70

3.18

3.52

Anhui

8.37

5.00

3.36

Fujian

9.42

5.60

3.82

Jiangxi

7.68

4.79

2.89

Shandong

6.69

4.75

1.94

Henan

6.85

4.88

1.97

Hubei

6.51

4.33

2.18

Hunan

5.83

3.49

2.34

Guangdong

5.65

5.95

-0.30

Guangxi

8.39

7.87

0.52

Sichuan

4.46

3.44

1.02

Guizhou

8.14

4.12

4.02

Yunan

6.77

5.59

1.18

Shaanxi

4.09

3.94

0.15

Gansu

7.85

4.77

3.08

Qinghai

6.66

2.61

4.05

Ningxia

9.32

6.84

2.47

Xinjiang

8.23

6.66

1.57

National

6.60

4.57

2.03

Notes: The constant price index was calculated by the authors using the official price and output data (no adjustments were made on meat and fishery data) and 1980 constant prices in aggregation. The TT2 index is the Törnqvist-Theil index constructed by the authors with adjusted livestock and fishery data.

Table 3: Measures of Input Growth at the National Level, 1952=100

Year

1980 Shares

Wen (or Wein)

TT

1952

100

100

100

1953

111

105

103

1954

119

109

106

1955

127

111

108

1956

135

114

112

1957

146

119

116

1958

144

112

107

1959

143

107

104

1960

146

108

106

1961

150

109

111

1962

162

115

116

1963

176

122

122

1964

189

129

128

1965

199

138

132

1966

215

150

139

1967

219

152

141

1968

216

147

140

1969

226

155

144

1970

240

164

150

1971

254

175

157

1972

261

181

160

1973

271

191

164

1974

274

190

165

1975

281

196

168

1976

284

200

169

1977

288

206

171

1978

301

228

179

1979

312

247

185

1980

316

257

187

1981

316

261

187

1982

322

274

191

1983

323

283

192

1984

324

286

192

1985

320

283

190

1986

326

296

193

1987

330

305

196

1988

336

317

199

1989

346

334

205

1990

355

351

210

1991

362

365

214

1992

363

370

215

1993

364

384

216

1994

368

399

218

1995

375

420

223

1996

380

437

227

1997

388

453

232

Annual Growth Rate

1952-78

4.33

3.23

2.26

1979-97

1.22

3.42

1.25

1952-97

3.06

3.41

1.88

Notes: The 1980 constant share index was constructed using the 1980 shares: 0.22 for land, 0.41 for labor, 0.072 for chemical fertilizer, 0.011 for pesticides, 0.031 for seed, 0.082 for feed, 0.047 for machinery, 0.081 for animals, and 0.040 for irrigation. The Wen index was constructed using the following weights: 0.35 for labor, 0.36 for land, 0.09 for capital (aggregation of both machinery and animal capitals), and 0.20 for current inputs (aggregation of chemical fertilizer, pesticides, seed, and feed). The TT index is constructed using the Törnqvist-Theil approach.

Table 4: Measures of Input Growth at the Regional Level, 1979-1997

Province1980 Shares

TT

Overestimation

Annual Growth Rates

Hebei

1.33

1.42

-0.09

Shanxi

0.91

0.87

0.04

Inner Mongolia

1.55

2.18

-0.63

Liaoning

0.54

0.33

0.22

Jilin

1.58

1.82

-0.25

Heilongjiang

1.06

1.24

-0.18

Jiangsu

0.14

0.28

-0.14

Zhejiang

-0.43

-0.46

0.02

Anhui

1.73

1.67

0.06

Fujian

0.74

0.83

-0.09

Jiangxi

1.17

1.19

-0.03

Shandong

0.81

0.78

0.03

Henan

1.72

1.90

-0.18

Hubei

0.43

0.78

-0.35

Hunan

0.80

0.70

0.10

Guangdong

0.09

-0.07

0.16

Guangxi

1.44

1.65

-0.21

Sichuan

0.75

0.82

-0.08

Guizhou

2.63

2.49

0.14

Yunan

2.01

2.16

-0.16

Shaanxi

1.40

1.67

-0.27

Gansu

2.05

2.09

-0.05

Qinghai

0.94

0.99

-0.04

Ningxia

3.16

2.46

0.70

Xinjiang

1.38

1.60

-0.22

National

1.22

1.25

-0.03

Notes: The 1980 constant share index was constructed using the 1980 shares. The TT index is constructed using the Törnqvist-Theil approach.

Table 5: Measures of TFP Growth at the National Level, 1950-97

Year

1980 Constant Prices

TT1

TT2

1952

100

100

100

1953

90

97

97

1954

87

97

97

1955

89

104

104

1956

88

105

105

1957

82

103

103

1958

87

116

116

1959

78

107

107

1960

63

87

87

1961

54

74

74

1962

55

77

77

1963

55

80

80

1964

59

85

85

1965

61

89

89

1966

62

93

93

1967

63

95

95

1968

65

97

97

1969

65

98

98

1970

62

95

95

1971

61

94

94

1972

59

92

92

1973

61

96

96

1974

64

100

100

1975

65

101

101

1976

63

99

99

1977

63

98

98

1978

67

103

103

1979

69

106

106

1980

71

108

108

1981

76

115

116

1982

82

123

125

1983

87

130

131

1984

96

141

143

1985

102

147

151

1986

102

146

152

1987

107

152

156

1988

109

150

151

1989

103

154

153

1990

119

163

162

1991

122

167

164

1992

129

174

169

1993

141

185

174

1994

157

195

176

1995

170

209

183

1996

176

221

192

1997

176

225

190

Annual Growth Rates

1952-78

-1.55

0.12

0.12

1979-97

5.34

4.26

3.28

1952-97

1.27

1.82

1.44

Table 6: Measures of TFP Growth at the Regional Level, 1979-1997

Province

Constant Price

TT2

Overestimation

Annual Growth Rates

Hebei

5.90

3.76

2.14

Shanxi

4.52

2.43

2.09

Inner Mongolia

8.42

5.03

3.39

Liaoning

5.61

3.97

1.64

Jilin

3.45

1.87

1.58

Heilongjiang

5.01

3.33

1.68

Jiangsu

5.68

3.94

1.74

Zhejiang

6.99

3.37

3.62

Anhui

6.57

3.34

3.23

Fujian

8.54

4.59

3.95

Jiangxi

6.44

3.54

2.90

Shandong

5.82

3.84

1.98

Henan

5.13

3.05

2.07

Hubei

6.00

3.45

2.55

Hunan

4.99

2.72

2.26

Guangdong

5.47

5.68

-0.21

Guangxi

6.87

6.02

0.85

Sichuan

3.69

2.57

1.12

Guizhou

5.53

1.88

3.64

Yunan

4.78

3.49

1.29

Shaanxi

2.74

2.36

0.38

Gansu

5.78

2.79

3.00

Qinghai

5.66

1.67

3.99

Ningxia

6.16

4.40

1.76

Xinjiang

6.78

4.94

1.84

National

5.34

3.28

2.06

Notes: The 1980 constant price (or share) index is constructed using the 1980 constant price output index and 1980 constant share input index. TheTT2 TFP index is constructed using the TT2 output index from Table 2 and the TT input index from Table 4.

Figure 1 Aggregation bias in output

Figure 2 Aggregation bias in input

1952

1957

1962

1967

1972

1977

1982

1987

1992

1997

0

0.2

0.4

0.6

0.8

1

irrigation

animals

machinery

feed

seed

pesticides

c. fertilizer

labor

land

Figure 3: Changes in Input Shares

1979

1982

1985

1988

1991

1994

1997

0.16

0.18

0.2

0.22

0.24

0.26

0.28

0.3

0.32

G1

G0

Figure 4: Measures of Regional Inequality

Note: G0 and G1 are Gini coefficients calculated from official SSB data and our TT output growth index, respectively.

Notes

*The authors are senior research fellow and post-doctoral fellow, respectively, at the International Food Policy Research Institute, Washington, D.C. They are grateful for the comments from D. Gale Johnson, and participants of the 75th Annual Western Economics Association International Conference, June 29-July 3, Vancouver, Canada. Funding from the Australian Center for International Agricultural Research (ACIAR), the Government of Japan, and the National Natural Science Foundation of China is acknowledged.

1. State Statistical Bureau, China's Statistical Yearbook (Beijing: China Statistical Press, 1998).

�. The major studies on Chinese Agriculture using the official output data include Wiens, Thomas, "Technical Change," in Randolph Barker et al. (eds), The Chinese Agricultural Economy (Boulder: Westview Press, 1982); Lardy, R. Nicholas, Agriculture in China's Modern Economics Development (Cambridge: Cambridge University Press ,1983); Perkins, Dwight and Shahid Yusuf, Rural Development in China (Baltimore: The Johns Hopkins University Press, 1984); Tang, Anthony, An Analytical and Empirical Investigation of Agriculture in Mainland China, 1952-1980 (Taibei: Chung-Hua Institution for Economics Research, 1984); Lin, Justin Yifu, "The Household Responsibility System in China's Agricultural Reform: A Theoretical and Empirical Study", Economic Development and Cultural Change 36 (1988): 200-224; McMillan, John, John Whalley, and Lijing Zhu, "The Impact of China's Economic Reforms on Agricultural Productivity Growth", Journal of Political Economy No. 97 (1989), pp.781-807; Fan, Shenggen, Regional Productivity Growth in China's Agriculture (Boulder: Westview Press, 1990); Wen, Guanzhong J., "Total Factor Productivity Change in China's Farming Sector: 1952-1989", Economic Development and Cultural Change Vol 42 (1993), pp. 1-41; World Bank, China: Options for Reform in the Grain Sectors, A World Bank Country Study, Washington, D.C.: World Bank (1991); Fan, Shenggen, "Effects of Technological Change and Institutional Reform on Production Growth in Chinese Agriculture", American Journal of Agricultural Economics Vol. 73, No. 2 (1991), pp. 266-275; Lin, Justin Yifu, "Rural Reforms and Agricultural Growth in China", American Economic Review, Vol. 82 (1992), No. 1, pp. 34-51; Fan, Shenggen and Phillip Pardey, “Research, Productivity, and Output Growth in Chinese Agriculture,” Journal of Development Economics Vol. 53 (1997), pp. 115-137; and Zhang, Bin, and Collin Carter, “Reforms, the Weather, and Productivity Growth in China’s Grain Sector.” American Journal of Agricultural Economics Vol. 79 (November 1997): 1266-77.

�. The agency has been renamed as National Statistical Bureau since 2000.

�. The State Statistical Bureau has also recently published GDP growth for the agriculture sector. But it suffers the same problem as GVAO, i.e., measured in constant prices. See SSB, Calculation Methods of China Annual GDP (Beijing: China Statistical Publishing House, 1997).

�. Diewert, W.E., "Exact and Superlative Index Numbers," Journal of Econometrics, 4:115-146 (1976); Lau, L.J., "On Exact Index Numbers," Review of Economics and Statistics Vol. 61, No. 1: 73-82 (1979); Jorgenson, Dale, Productivity Volume 1: Post U.S. Economic Growth (Cambridge, Massachusetts: The MIT Press, 1995); and Jorgenson, Dale, Productivity Volume 2: International Comparisons of Economic Growth (Cambridge, Massachusetts: The MIT Press, 1996).

�. GVAO measured in current and constant prices are reported by various yearbooks published by State Statistical Bureau (SSB) and Ministry of Agriculture (MOA), for example, China Statistical Yearbook (SSB), China Agricultural Yearbook (MOA), China Rural Statistical Yearbook (SSB), and China Agricultural Statistical Materials (MOA). Since 1949, the SSB has measured output using five base prices, i.e., 1952 base prices for 1949-1957, 1957 for 1958-1970, 1970 for 1971-1981, 1980 for 1981-1990, and 1990 since 1990.

�. Alston et al. have also demonstrated the potential bias in aggregation of inputs and outputs when technical change is both present and absent. See Alston, Julian M., George W. Norton, and Philip G. Pardey, Science Under Scarcity: Principles and Practice for Agricultural Research Evaluation and Priority Setting (Ithaca: Cornell University Press, 1995).

�. Richter, M.K., "Invariance Axioms and Economic Indexes," Econometrica Vol. 34. No. 4: 739-55 (1966).

�. The State Statistical Bureau adjusted its livestock production data for 1996 in 1998 China Statistical Yearbook based on the Agriculture Census. The pork output was adjusted downward by 28%, beef by 39%, poultry by 27%, and mutton by 32%.

�. Fuller et al. adjusted official SSB data in two scenarios. In the first scenario, they assume that the proportion of total livestock products consumed outside of the household remains constant. Likewise, the yield of retail cuts from cattle, swine, and sheep carcasses remains constant over time. They also assume that the reported trade statistics are accurate and that stock levels remain constant. Finally, the ratio of production-based per capita consumption to survey averages over the 1985–1987 period is assumed to capture the inaccuracies introduced by away-from home consumption and stock changes; thus, this ratio is held constant near the 1985–1987 average.

They construct the Scenario 2 estimates using Scenario 1 assumptions regarding carcass yields, stocks, and trade; however, the under-reporting caused by away-from-home (AFH) consumption in urban areas is assumed to increase proportional to the change in real per capita expenditures for urban households. Urban AFH consumption increases urban per capita meat consumption by 30 percent in 1993, rising to 35 percent in 1996, thus attaining a level roughly equivalent to average AFH consumption in the U.S. in 1970. Rural AFH consumption also rises proportional to the increase in real per capita expenditure for rural households, increasing rural per capita meat consumption by 1 percent in 1996.

In this study, we use their scenario 2 estimates to adjust meat and egg output. The adjustments began in 1980 assuming that the overestimation or underestimation is zero, and then increases linearly to the level Fuller et al constructed for 1985 to avoid a disjoint.

�. It would be desirable to disaggregate fishery into individual products. But it is practicably impossible due to large number of various fishery products. It does not cause bias in aggregating total output to great extent because fishery accounts only 9% of total production value even in 1997.

�. Lu estimated that official fishery output in China is over reported by 69% in 1997.

�. State Statistical Bureau. China's Statistical Yearbook (Beijing: China Statistical Press, 1981-98); and Editorial Committee of China's Agricultural Yearbooks, China's Agricultural Yearbook (Beijing: China's Agricultural Press, 1981-98).

�. State Statistical Bureau, China Domestic Marketing Statistical Yearbook, (Beijing: China Statistical Press, 1993-98); and USDA, Agricultural Statistics of the People's Republic of China, 1949-90 (USDA 1993).

�. Labor used in forestry production is excluded using the share of forestry production value in total agricultural production value.

�. Editorial Committee of the Handbook of Agricultural Technical Economics, Handbook of Agricultural technical Economics. Beijing: Agricultural Press.

�. State Price Bureau, Production Cost Survey (State Price Bureau, 1953-98).

�. The average number of working days is 300 in 1978, and then decline by 2 days per year. This is because farmers are working few days, and spend more time in non-farm activities or leisure. Therefore, number of labor-days used in agricultural production has declined for crop, cash and livestock production (Production and Cost Survey).

�. For example, Tang (1984), Wiens (1982), and Wen (1993).

�. Fan, Shenggen, and Vernon Ruttan. 1992. "Technical Change in Centrally Planned Economies," Agricultural Economics (The Journal of International Association of Agricultural Economists) 6: 301-314.

�. The factor shares presented here are roughly comparable with those reported by Fan (1990).

�. Wen (1993) used the fixed weights in his measures: 0.35 for labor, 0.09 for capital, 0.36 for land, and 0.20 for current input. Later Jin et al. (1999), and Carter et al. (1999) either used Wen’s weights or 1995 constant prices in constructing their TFP indexes at the crop level. See Jin, Songqing, Scott Rozelle, Erika Meng, Ruifa Hu, and Jikung Huang, “Genetic Diversity and Total Factor Productivity: The Case of Wheat in China,” paper presented at the Symposium on China’s Agricultural Trade and Policy: Issues, Analysis and Global Consequences, San Francisco, June 25-26, 1999; and Carter Colin, Jing Chen, and Baojin Chu, “Agricultural Productivity Growth in China: Farm Level versus National Measurement,” paper presented at the Symposium on China’s Agricultural Trade and Policy: Issues, Analysis and Global Consequences, San Francisco, June 25-26, 1999.

�. The reason why 1980 constant share input index is different from Wen’s (or Wien as his central case) is because our shares in 1980 are very different from those of Wen’s. For example, in our paper, shares are: 0.199 for land, 0.422 for labor, 0.072 for chemical fertilizer, 0.011 for pesticides, 0.031 for seed, 0.107 for feed, 0.047 for machinery, 0.071 for animals, and 0.04 for irrigation. But for Wen, they are: 0.36 for land, 0.35 for labor, 0.09 for capital, and 0.2 for current input. Wen’s land and labor shares are larger than those in our paper, and these two inputs grew relatively slowly than other inputs, leading to a slower growth in total input in Wen’s aggregation.

�. At least the following recent studies use output values in constant prices for inequality analysis: Tsui, Kai-yuen, “China’s Regional Inequality, 1952-1985,” Journal of Comparative Economics, Vol. 15 (April 1991), 1-21; Chen, Jian and Belton Fleisher, “Regional Income Inequality and Economic Growth in China,” Journal of Comparative Economics, Vol. 22 (1996), pp.141-164; Yang, Dannis Tao and Hao Zhou, “Rural-Urban Disparity and Sectoral Labor Allocation in China,” Journal of Development Studies, Vol. 35(1999), pp. 105-133; Kanbur, Ravi and Xiaobo Zhang, “Which Regional Inequality? The Evolution of Rural-Urban and Inland-Coastal Inequality in China from 1983 to 1995,” Journal of Comparative Economics, Vol. 27 (1999), pp. 686-701.

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